Murakami Sale
 
 

Recently Viewed clear list


Q&A | August 19, 2014

Richard Kadrey: IMG Powell’s Q&A: Richard Kadrey



Describe your latest book. The Getaway God is the sixth book in the Sandman Slim series. In it, the very unholy nephilim, James Stark, aka Sandman... Continue »
  1. $17.49 Sale Hardcover add to wish list

spacer
Qualifying orders ship free.
$85.50
New Trade Paper
Ships in 1 to 3 days
Add to Wishlist
available for shipping or prepaid pickup only
Available for In-store Pickup
in 7 to 12 days
Qty Store Section
25 Remote Warehouse Database- Design

Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

by

Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) Cover

 

Synopses & Reviews

Publisher Comments:

Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining'including, i.e., the rule onions, potatoes] -> beef] found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, he or she is also likely to buy beef. The authors inlcude both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.

Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download.

The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.

* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques

* Algorithmic methods at the heart of successful data mining'including tired and true methods as well as leading edge methods

* Performance improvement techniques that work by transforming the input or output

* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization'in an updated, interactive interface.

Book News Annotation:

The third edition of this practical guide to machine learning and data mining is fully updated to account for technological advances since its previous printing in 2005 and is now even more closely aligned with the use of the Weka open source machine learning, data mining and data modeling application. Beginning with an introduction to data mining, the volume explores basic inputs, outputs and algorithms, the implementation of machine learning schemes and in-depth exploration of the many uses of the Weka data analysis software. Numerous illustration, tables and equations are included throughout and additional resources are available through a companion website. Witten, Frank and Hall are academics with the department of computer science at the University of Waikato, New Zealand, the home of the Weka software project. Annotation ©2011 Book News, Inc., Portland, OR (booknews.com)

Synopsis:

If you have data you want to analyze and understand, this book and the associated WEKA Toolkit will get you the results you seek!

Synopsis:

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.

Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.

*Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

About the Author

<>Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann.Eibe Frank lives in New Zealand with his Samoan spouse and two lovely boys, but originally hails from Germany, where he received his first degree in computer science from the University of Karlsruhe. He moved to New Zealand to pursue his Ph.D. in machine learning under the supervision of Ian H. Witten, and joined the Department of Computer Science at the University of Waikato as a lecturer on completion of his studies. He is now an associate professor at the same institution. As an early adopter of the Java programming language, he laid the groundwork for the Weka software described in this book. He has contributed a number of publications on machine learning and data mining to the literature and has refereed for many conferences and journals in these areas.>Mark A. Hall was born in England but moved to New Zealand with his parents as a young boy. He now lives with his wife and four young children in a small town situated within an hour’s drive of the University of Waikato. He holds a bachelor’s degree in computing and mathematical sciences and a Ph.D. in computer science, both from the University of Waikato. Throughout his time at Waikato, as a student and lecturer in computer science and more recently as a software developer and data mining consultant for Pentaho, an open-source business intelligence software company, Mark has been a core contributor to the Weka software described in this book. He has published a number of articles on machine learning and data mining and has refereed for conferences and journals in these areas.

University of Waikato, Hamilton, New Zealand. Recipient of the 2005 ACM SIGKDD Service Award.

Table of Contents

PART I: Introduction to Data Mining Ch 1 What's It All About? Ch 2 Input: Concepts, Instances, Attributes Ch 3 Output: Knowledge Representation Ch 4 Algorithms: The Basic Methods Ch 5 Credibility: Evaluating What's Been Learned PART II: Advanced Data Mining

Ch 6 Implementations: Real Machine Learning Schemes Ch 7 Data Transformation Ch 8 Ensemble Learning Ch 9 Moving On: Applications and Beyond PART III: The Weka Data MiningWorkbench Ch 10 Introduction to Weka Ch 11 The Explorer Ch 12 The Knowledge Flow Interface Ch 13 The Experimenter Ch 14 The Command-Line Interface Ch 15 Embedded Machine Learning Ch 16 Writing New Learning Schemes Ch 17 Tutorial Exercises for the Weka Explorer

Product Details

ISBN:
9780123748560
Author:
Witten, Ian H.
Publisher:
Morgan Kaufmann Publishers
Subject:
Database Management - General
Subject:
Database design
Series:
The Morgan Kaufmann Series in Data Management Systems
Publication Date:
20110131
Binding:
TRADE PAPER
Language:
English
Pages:
664
Dimensions:
9.25 x 7.5 in

Other books you might like

  1. Python for Data Analysis New Trade Paper $39.99

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Computers and Internet » Database » Design
Computers and Internet » Software Engineering » Software Management
Science and Mathematics » Agriculture » General
Science and Mathematics » Biology » Zoology » General
Science and Mathematics » Mathematics » General

Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) New Trade Paper
0 stars - 0 reviews
$85.50 In Stock
Product details 664 pages Morgan Kaufmann Publishers - English 9780123748560 Reviews:
"Synopsis" by , If you have data you want to analyze and understand, this book and the associated WEKA Toolkit will get you the results you seek!
"Synopsis" by , Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.

Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.

*Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

spacer
spacer
  • back to top
Follow us on...




Powell's City of Books is an independent bookstore in Portland, Oregon, that fills a whole city block with more than a million new, used, and out of print books. Shop those shelves — plus literally millions more books, DVDs, and gifts — here at Powells.com.